The Mathematica book (4th edition)
The Mathematica book (4th edition)
Illustrating evolutionary computation with Mathematica
Illustrating evolutionary computation with Mathematica
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Simulating Neural Networks with Mathematica
Simulating Neural Networks with Mathematica
A neural network ensemble method with jittered training data for time series forecasting
Information Sciences: an International Journal
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Reducing the search space in evolutive design of ARIMA and ANN models for time series prediction
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
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The design of models for time series prediction has found a solid foundation on statistics. Recently, artificial neural networks have been a good choice as approximators to model and forecast time series. Designing a neural network that provides a good approximation is an optimization problem. Given the many parameters to choose from in the design of a neural network, the search space in this design task is enormous. When designing a neural network by hand, scientists can only try a few of them, selecting the best one of the set they tested. In this paper we present a hybrid approach that uses evolutionary computation to produce a complete design of a neural network for modeling and forecasting time series. The resulting models have proven to be better than the ARIMA and the hand-made artificial neural network models.